Analytical Lett 2005

8/6/2019 Analytical Lett 2005

http://slidepdf.com/reader/full/analytical-lett-2005 1/18

CHEMOMETRICS

Data Compression for a VoltammetricElectronic Tongue Modelled with

Artificial Neural Networks

Laura Moreno-Baron, Raul Cartas, Arben Merkoci, and

Salvador Alegret

Sensors and Biosensors Group, Chemistry Department, Autonomous

University of Barcelona, Bellaterra, Catalonia, Spain

Juan M. Gutierrez, Lorenzo Leija, Pablo R. Hernandez, and

Roberto Mun ˜ oz

Bioelectronics Section, Department of Electrical Engineering, Cinvestav,Mexico City, Mexico

Manuel del Valle

Sensors and Biosensors Group, Chemistry Department, Autonomous

University of Barcelona, Bellaterra, Catalonia, Spain

Abstract: In the study of voltammetric electronic tongues, a key point is the preproces-

sing of the departure information, the voltammograms which form the response of the

sensor array, prior to classification or modeling with advanced chemometric tools. This

work demonstrates the use of the discrete wavelet transform (DWT) for compacting

these voltammograms prior to modeling. After compression, a system based on

artificial neural networks (ANNs) was used for the quantification of the electroactive

substances present, using the obtained wavelet decomposition coefficients as their

inputs. The Daubechies wavelet of fourth order permitted an effective compression

Received 17 June 2005; accepted 22 June 2005

Financial support for this work was provided by the MECD (Madrid, Spain) throughproject CTQ2004-08134, by CONACYT (Mexico) through project 43553, and by the

Department of Universities and the Information Society (DURSI) from the Generalitat

de Catalunya.

Address correspondence to Manuel del Valle, Sensors and Biosensors Group,

Chemistry Department, Autonomous University of Barcelona, Bellaterra, Catalonia

E-08193, Spain. E-mail: [email protected]

Analytical Letters, 38: 2189–2206, 2005

Copyright# Taylor & Francis, Inc.

ISSN 0003-2719 print/1532-236X online

DOI: 10.1080/00032710500259342

2189



up to 16 coefficients, reducing the original dimension by ca. 10 times. The case studied

is a mixture of three oxidizable amino acids:tryptophan, cysteine, and tyrosine. With

the reduced information, one ANN per specie was trained using the Bayesian regular-

ization algorithm. The proposed procedure was compared with the more conventional

treatments of downsampling the voltammogram, or its feature extraction employing

principal component analysis prior to ANNs.

Keywords: Voltammetric electronic tongue, discrete wavelet transform, artificial

neural networks, PCA, oxidizable aminoacids

INTRODUCTION

We have been attending during recent years to the success of the concept

of electronic tongues in the field of chemical sensors. This case is one of

the more clear benefits accounted for in the combination of chemometrics

and electrochemical sensors (Pravdova 2002), which was foreseen as an

excellent way to improve sensor performance (Lavine 2002). An accepted

definition of electronic tongue (Holmberg 2004) entails an analytical instru-

ment comprising an array of nonspecific, poorly selective, chemical sensors

with cross-sensitivity to different compounds in a solution, and an appropriatechemometric tool for the data processing. For the analysis of liquid samples,

there are two main kinds of electronic tongues, those employing potentio-

metric sensors (Gallardo 2003) and those employing voltammetric sensors

(Winquist 1997). The latter usually employ arrays of voltammetric electrodes,

for example, a number of different metallic electrodes, or a number of

modified electrodes (Apetrei 2004). From the conceptual point of view, a vol-

tammetric system with a single electrode can also be considered an electronic

tongue, as the dominating point here is the high order measuring information

fed to the computer-processing tool.Moreover, regarding this feature, the complexity of the input information,

it makes enormously cumbersome any chemometrical stage, becoming a cri-

tical issue in voltammetric electronic tongues. In this way, a crucial point in

this field is the reduction of the data, prior to classification or calibration.

The use of artificial neural networks (ANNs) is widely accepted to build the

calibration model of electronic tongues (Krantz-Rulcker 2001). When the

input information is of the voltammetric type, it becomes difficult to

correctly build and adjust a network with hundreds of input nodes, as needed

by these voltammograms. Again, the way to solve this bottleneck is throughthe preprocessing of the original signal in order to reduce its dimensions.

In addition to size reduction, compression is intended to extract signi-

ficant features from the departure information, besides the elimination of

irrelevant content, such as noise or redundancies (Simons 1995; Despagne

1998). Further advantages of such pretreatment can be an increased training

speed, a reduction of memory needs, better generalization ability of the

L. Moreno-Baron et al.2190



model, enhanced robustness versus noise, and simpler model representations

(Despagne 1998).

A widely used method for data compression is principal componentanalysis (PCA). The method intends to summarize almost all variance

contained in the departure information on a fewer number of directions (the

PCs) with new coordinates called scores, obtained after data transformation.

These axes or directions have the property to be mutually orthogonal, which

facilitates the use of linear regression models (principal components

regression, PCR). ANNs will not benefit from the orthogonality of input

variables, but applications in quantitative analysis can still be developed

employing the PC scores as input data (Borggaard 1992; de Carvalho

2000). In practice, PC scores have been successfully used as inputs, becauseall relevant information from a huge spectrum or a voltammogram is

reduced to a few PCs depending on the correlation of the original data. One

limitation of the treatment is that it can fail to preserve the nonlinearity of a

data set, as it is a linear projection technique. If there are some nonlinear

characteristics in the departure information, these will be considered as pertur-

bations or noise and will not be described by the first PCs as in a linear case.

Alternatively to PCA, it is possible to use Fourier analysis (Gemperline

1997), Hadamard transform (Dathe 1996), and discrete wavelet transform

(DWT) (Collantes 1997), or the most common technique of downsamplingto preprocess input data before ANN modeling. The DWT is a processing

tool that yields a series of coefficients as a result of relating an original

signal with a family of functions that are scaled and translated versions of a

base function known as mother wavelet (Leung 1998). Its most attractive

feature is its ability to optimally describe temporal information from the

spectrum, while Fourier decomposition is global (Shao 2003). This feature

allows DWT to describe nonstationary signals in a better way than does

Fourier transform, which employs periodical sine and cosine functions

(Chau 2004).When working with voltammetric electronic tongues, different options

have been attempted to reduce the complexity of the acquired information.

Seminal works of this research topic employed the fitting to different para-

metric models having more or less physical meaning (Simons 1995).

Different measurement compression techniques were evaluated with the vol-

tammetric electronic tongue developed by the group of Winquist in Sweden

(Winquist 1997; Krantz-Rulcker 2001) and used for classification; the

measurements of this electronic tongue are the responses of an array of

metallic working electrodes of different nature to a set of voltage pulses.A first work (Holmin 2001) compared the hierarchical principal

component analysis, an evolution of PCA, against the DWT for the classifi-

cation task of beverages and foods. In the same work, a third procedure that

yielded the best results was evaluated; it used a parametric model based on

a sum of exponential decays with some electrochemical fundamentals. A

second contribution by Artursson (2002a) studied a model based on the sum

Data Compression for Voltammetric Electronic Tongue 2191



of two exponential decays to compress the original voltammetric signal by a

factor of ca. 100. The reduced signal was then used for the classification,

employing PCA, of different aqueous samples and beverages. An additionalwork from the same author (Artursson 2002b) evaluated the use of

the DWT to reduce the raw data of a voltammetric e-tongue applied at a

bottling plant of drinking water. These contributions are closely related

with their equivalents in the field of gas sensors (systems known as

electronic noses), where the DWT has been employed for feature

extraction of dynamic or thermally modulated signals (Distante 2002;

Ionescu 2002).

Apart from these works related to classification, there are also in the lit-

erature several contributions dealing with the resolution of overlapped signalsobtained with electroanalytical procedures and employing the DWT for this

purpose (Shao 2001). These works are aimed to the compression of raw

data, altogether with the retention of information needed to correlate them

with the concentrations present. In this way, these works are more related

with the computing needed to obtain the quantitative application of the

e-tongue data. Different alternatives for the quantification of the electroactive

components present are attempted, all of which aimed to the extraction of sig-

nificant features of an overlapped voltammetric signal. Almost all of these

works are focused on the resolution of metals mixed in a solution, as is thecase of the continuous WT applied to the determination of mixtures of

cadmium and indium (Nie 2001), or the derivative WT used for determining

mixtures of indium and cadmium or dopamine and ascorbic acid (Zhang

2004). Also remarkable is the development of an instrument that employs

the DWT online and displays the resolution of lead and thallium, or

cadmium and indium (Shao 2000) mixed in a solution.

The utility of the DWT was demonstrated in the extraction of significant

features and the subsequent use of them for the quantitative modeling of vol-

tammetric signals employing multivariate calibration procedures. These con-tributions studied the optimum wavelet type to reduce the voltammogram and

used the compressed information to perform the modeling with partial least

squares (PLS) regression or with ANNs (Cocchi 2003a). In these different

works, the case under study resolved a two-component system with two

metallic ions: thallium and lead (Palacios-Santander 2003; Cocchi 2003b).

Recently in our laboratory, an equivalent system was developed, in

which the original information in the voltammogram is compressed

employing the DWT, and the reduced information is used for ANN

modeling (Moreno-Baron 2005).The present work compares the performance of this alternative, the use of

DWT as compression tool prior to the quantitative modeling of voltammetric

electronic tongues employing ANN, with the more classical PCA þ ANN

combination (Ensafi 2002), or with the more straightforward alternative,

just the downsampling to reduce the dimensionality of the raw data, plus

ANN modeling (Gutes 2005).




EXPERIMENTAL

Reagents and Materials

All chemicals for electrolyte and the stock of amino acid solutions, tryptophan

(Trp), cysteine (Cys), and tyrosine (Tyr), were purchased from Merck as pro-

analysis grade. The support electrolyte solution consisted of 0.1 M potassium

chloride þ 0.1 M phosphate solution (pH was adjusted to 7.5). Synthetic

mixtures for the evaluation of the voltammetric method were prepared from

0.1 M stock solutions of each amino acid.

Apparatus

A PGSTAT 20 Autolab potentiostat with a Pt working electrode was used

for differential pulse voltammetric measurements. The resulting voltammetric

data consisted of current intensities recorded in the range of potentials

from 0.4 to 1.0 V in steps of 0.00365 V. Hence, 164 data points per

sample were recorded, which formed the voltammograms used in further

analysis. The modulation amplitude was 0.025 V, the modulation time

was 70 ms, and the pulse interval was 300 ms. No preconditioning was

performed.

For the series of synthetic samples, microvolumes of each amino acid

mixture solution were added to 25 ml of the support electrolyte solution gen-

erating the different sample series. A magnetic stirrer was used to homogenize

the solution prior to measurement.

Procedure

Three series of synthetic solutions were prepared for Trp, Cys, and Tyranalysis. For each analyte, six concentration levels were considered as

follows: 5.0, 10, 20, 25, 30, and 35mM for Cys and Tyr, and 2.0, 6.0, 10,

14, 17, and 21 mM for Trp. Interferences were studied at two levels: 10 and

25mM for Trp and Cys; 5.0 and 34 mM for Tyr. As a result, each analyte

series was composed of 24 mixture solutions, and the set of voltammograms

processed 72.

Software

DWT as well as ANN modeling were implemented employing Matlab

version 6.1 (MathWorks, Natick, MA) with the aid of its Neural Network

(version 4.0) and Wavelet (version 2.0) toolboxes. The PCA treatment was

done employing the statistical program Minitab, release 14 (Minitab Inc.,

State College, PA).




RESULTS AND DISCUSSION

The procedures for data reduction employing DWT, PCA, and downsamplingwere set up and fine-tuned before the voltammograms were coupled to the

modeling system based on ANNs. The performances of the three alternatives

were compared after training, using the input training data and an external test

subset. The analytical case studied was the simultaneous direct determination

of oxidizable amino acids, a common application in animal feed analysis.

Figure 1 shows a typical voltammogram, corresponding to one of the

original input data. As can be observed, a degree of overlapping, together

with background oxidation, makes the estimation of the components an inter-

esting issue.

Conditioning of the Information

The departure universe of data used for building the calibration model

consisted of an input matrix, formed by 72 samples (columns) with 164

current values (rows) for each one, plus the corresponding output matrix,

formed by 72 samples with three concentration values (rows) for each one.

Figure 1. Example of the overlapped-signal voltammogram of oxidizable amino

acids studied in this work. Concentrations on the curve are (Trp, Cys, Tyr) 5.2, 25,

and 34mM, respectively. Oxidation zones for the three considered amino acids are indi-

cated on the figure.




The first preprocessing was done with the DWT using the Daubechies

mother wavelet of fourth order and taking the decomposition to a fourth

level. The mother wavelet, order, and decomposition level were chosenbased on a compromise between the number of approximation coefficients

obtained at each decomposition level and the degree of similarity between

the original voltammogram and the one recovered with these coefficients, as

was done in a previous work (Moreno-Baron 2005). The fewest number of

coefficients and the highest similarity were the goals. With this processing,

the size of the input information was reduced from 164 points per voltammo-

gram to only 16; therefore, the information was compressed by a factor

slightly larger than 10.

To reduce the input information employing PCA, the analysis showed thatmore than 95% of input variance could be explained with just the first three

PCs; nevertheless, in order to compare the efficiency with the DWT com-

pression, the first 16 PCs were taken, including in this way more information

than strictly necessary.

Lastly, in the downsampling scheme, the input matrix was reduced by a

factor of 10, following the decimation procedure (Mitra 2001), in which

a signal is resampled at a lower rate after lowpass filtering. The filter has a

cutoff frequency of p / D, where p is a normalized frequency and D is the

downsampling factor. In this way, all three compression techniques yielded16 rows. No change was needed to the output matrix in any case.

After the three compression alternatives were done, the data were split

into two subsets for training and testing the neural networks. For the

training process, 75% of the total number of columns was taken, while

for testing, the remaining columns were used. All data were normalized

to the interval [21,1] to facilitate the convergence of the learning

algorithm. No internal validation subset was needed, due to the nature of

the used algorithm, as explained below.

ANNs

All the trained networks were of the feedforward backpropagation type,

identical in structure and topology. Three single output neural networks

were used in parallel, one for each modeled amino acid, as this scheme

reaches better results than a triple output network (Moreno-Baron 2005).

Each ANN had a three-layer structure: two hidden layers and one output

layer. The first hidden layer had six neurons and tangent-sigmoidal transferfunction, the second hidden layer had 24 neurons and logarithmic-sigmoidal

transfer function, and the output layer had a single neuron and linear

transfer function. The used training algorithm was the Bayesian regularization

algorithm. This algorithm has the particularity that it avoids overfitting

without the need to monitor the fitness degree of an internal validation subset

(Demuth 2001).




The goal for convergence training was a sum of squared error (SSE) of

0.001 in 200 or less training epochs. SSE was calculated as follows:

SSE ¼X N

j¼1

ðcexpected À ccalculatedÞ2 ð1Þ

where c is the concentration value and N the number of training samples.

Training of the ANNs

The programming for the ANN training was devised to improve the general-ization performance of the network, i.e., to correctly predict outputs related to

testing data. The Matlab program starts with the loading of the compressed

information for training and testing, then the network is initialized, and the

training is performed with the selected learning strategy. The network is

trained until it reaches the previously fixed SSE goal. After convergence

state is reached, the external test data are interpolated to check the

modeling ability for the considered chemical component. From the output

values, a prediction error using the absolute values of the residuals is

calculated. This error is a percentile relative absolute error (PRAE) definedby Eq. (2):

PRAE ¼1

M

X M

i¼1

cexpectediÀ ccalculatedi

cexpectedi

Á 100 ð2Þ

where M is the number of testing outputs.

At the beginning of the training process, it is assumed that the ANN does

not have a correct generalization ability with the external test data. Therefore,

the starting PRAE value before the training process begins is arbitrarilyassigned a value of 100%. If, at the end of training, the calculated PRAE

value is lower than the assumed value, the new is stored and taken as a

figure of merit to improve in the next training process.

Figure 2 shows the flowchart of the strategy used. Evidently, the training

process is iterative, because if the new PRAE value does not improve the

previous one, the former one is kept, and the training is reinitiated.

For the different trainings accomplished, PRAE values larger than 10%

were considered bad indication of the generalization capability of the network.

Results Obtained with the DWT Compression

The alternative that showed better generalization ability to data test was that

of ANNs employing DWT preprocessed voltammograms. Training for

each network lasted approximately 96 h to reach a minimum PRAE, and as




an additional 48 h of training did not improve this value, the best

achieved training was taken as the model, and that currently running was

aborted.

The PRAE values obtained for the three oxidizable amino acids Trp, Cys,

and Tyr were 6.76%, 5.90%, and 8.34%, respectively, each one obtained with

its own network. To visualize the modeling ability of the trained network, Fig. 3

Figure 2. Flowchart of the training process used for each artificial neural network

model.




Figure 3. Comparison of the obtained vs. expected results for the three considered

amino acids for DWT preprocessed input data. The dashed line corresponds to ideality

( y ¼ x), and the solid line is the regression of the comparison data. Graphs on the left

correspond to training and those on the right to external testing.




shows comparison graphs between obtained and expected concentration

values, where a correct behavior is clear.

A measure of the modeling performance can be deducted from the linear

regression of these plots, providing the best achievable case of a comparison

slope of 1 and a bias intercept of 0. In addition, correlation coefficients close to

one will indicate the achievements of the modeling.

The data of the linear regression analysis for training and testing subsets

corresponding to the three compression methods used in this work arepresented in Table 1. The table shows remarkable results of comparison

slopes, intercepts, and correlation coefficients for training data, which are

expected to obtain for a correctly trained network, and more significantly,

for external test data, a good indicator of the modeling ability.

Results Obtained with the PCA Compression

Although some works in the literature achieved correct results (de Carvalho2000; Ensafi 2002), networks trained with PCA compressed data did not

show good responses with the data test, which must be due to the difficulty

of the case studied. With exception of the network for Trp, which obtained

a PRAE value of 9.33%, the networks modeling Cys and Tyr yielded values

larger than the 10% limit, being 32.73% and 14.23%, respectively. Figure 4

shows the comparison graphs between obtained and expected concentration

Table 1. Linear regression parameters for the line ( y ¼ m . x þ b) that best fits the

plots of obtained vs. expected results for the networks trained with the three data

sets obtained with the WT, PCA, and downsampling processing techniques. The setswere split into two subsets for training and testing. Uncertainty intervals were calcu-

lated at 95% of confidence level

Amino

acid

Training Testing

m b m b

DWT

Trp 0.998+ 0.0023 1.1E-5+ 2.6E-05 1.012+ 0.0086 2.3E-4+ 1.1E-03

Cys 1.002+ 0.0018 8.9E-6+ 3.7E-05 1.043+ 0.0761 26.4E-4+ 1.7E-03

Tyr 0.994+ 0.0061 3.6E-5+ 1.2E-04 0.980+ 0.126 1.7E-4+ 2.7E-03

PCA

Trp 0.999+ 0.0019 5.7E-6+ 2.2E-05 0.897+ 0.128 7.7E-4+ 1.6E-03

Cys 0.987+ 0.014 2.4E-4+ 3.0E-04 0.865+ 0.395 4. 9E-3+ 8.9E-03

Tyr 0.999+ 0.0016 1.5E-5+ 3.2E-05 0.970+ 0.194 24.2E-4+ 4.4E-03

Downsampling

Trp 0.998+ 0.0021 0.028+ 2.5E-05 1.060+ 0.0130 20.96+ 1.7E-03

Cys 0.999+ 0.0012 0.020+ 2.5E-05 1.112+ 0.077 21.4+ 1.7E-03

Tyr 0.998+ 0.0014 0.031+ 2.8E-05 0.938+ 0.121 21.1+ 2.8E-03





amino acids for PCA preprocessed input data. The dashed line corresponds to ideality

( y ¼ x), and the solid line is the regression of the comparison data. Column at left

corresponds to training and column at right to testing.




values, for both training and testing sets. Corresponding data on Table 1

summarize the regression lines shown on the graphs, which are clearly

worse than those for the DWT case, specially when observing the modelingability for the external test.

Results Obtained with Downsampling

Networks employing simple downsampled information permitted better cali-

bration models to be built than those employing PCA. The PRAEs achieved

with this alternative were 6.6072%, 6.3986%, and 10.3958% for Trp, Cys,and Tyr, respectively. Comparison graphs of obtained vs. expected concen-

tration values for each amino acid are shown in Fig. 5. Their closeness to

ideality is again calculated from the linear regression lines, both for training

and for testing subsets, which are in Table 1. Although surpassed by the

DWT procedure, downsampling can be taken as a very simple approach that

yields acceptable models.

Table 2 summarizes the PRAEs obtained with the calibration models

based on the three different processing techniques used to compress the vol-

tammograms. The errors in the quantifications of Trp and Cys amino acidswere similar using either downsampling or DWT method. However, on quan-

tifying Tyr amino acid, the combination DWT–ANN performed better than

the combination of downsampling and ANN. DWT and downsampling

preprocessing techniques retain from the observed signal the information

contained at low frequencies. When downsampling is applied, the spectrum

is reduced from 2p /T v p /T to 2p / DT v p / DT. The parameter

T is the sampling rate, which has been given a value of 0.00365 V, the size

of the voltage step used in our voltammetry tests, and D is the reduction

factor. Despite wavelet approximation coefficients and downsampled voltam-mograms having 16 data points length for each one, the downsampled voltam-

mogram spectrum is only half the size of the postprocessed voltammogram

and has only eight frequency components.

DWT can be interpreted as a filter bank structured by levels. In the first level,

the original signal with length N and maximum spectral component frequency

( f max

) is low-pass filtered and high- or band-pass filtered and then downsampled

by a factor of two. The results of this process are subsignals halved in length and

bandwidth. The elements obtained from the low-pass filter after downsampling

are called approximation coefficients, and the ones obtained from the high-pass filter after downsampling are called detail coefficients. By bisecting the

bandwidth of each approximation subsignal, the frequency resolution is

doubled, i.e., it focuses on a finer band of frequencies. Likewise, downsampling

by a factor of two reduces the number of time samples and hence decreases time

resolution. This trade-off between time and frequency resolution is the mark of

the wavelet transform. Low frequency components are more difficult to





amino acids for downsampled preprocessed input data. The dashed line corresponds

to ideality ( y ¼ x), and the solid line is the regression of the comparison data. Column

at left corresponds to training and column at right to testing.




resolve in frequency domain, and thus, finer frequency resolution is desirable for

the processed voltammograms in this work.

The determination of Tyr amino acid from voltammograms is better when

the compacted signal presented at the input of the ANN carries spectral and

temporal information about the original signal. The upper graph in Fig. 6

shows one raw voltammogram, and the lower graph shows the same voltam-

mogram reconstructed from the 16 approximation coefficients obtained by

DWT using Daubechies wavelet of fourth order. Notice the smoothing

effect on the reconstructed signal. Correlation between original and recon-

structed signals was 0.98. The downsampled voltammogram is also plotted

in the lower graph over the reconstructed signal, and some points of it lie

outside the recovered voltammogram, mainly at the end of the signal.

Table 2. PRAE values obtained for the three alternatives

of preprocessing and ANN modeling

Preprocessing

technique PRAE1 PRAE2 PRAE3

Wavelet 6.76 5.90 8.34

PCA 9.33 32.73 14.23

Downsampling 6.6 6.39 10.39

Figure 6. (Upper) One raw voltammogram of the oxidizable amino acids. (Lower)

The previous signal reconstructed after wavelet processing (solid line) and downsam-

pling (asterisks). Note that differences in postprocessed signals are more manifest for

the lower current values.




CONCLUSIONS

This work demonstrated the use of voltammetric electronic tongues forthe simultaneous quantitative determination of three electroactive substances,

the oxidizable amino acids Trp, Cys, and Tyr. With the proposed strategy,

not only a very simple and direct measurement is obtained, but also the

correction of noise or baseline effects is achieved. The calibration model

is built, first pretreating the raw information, extracting significant

features employing the DWT, and next building appropriate ANNs as

calibration tools. The preprocessing of the voltammograms by DWT

has permitted the reduction of the amount of information needed to

represent its content in a factor of ca. 10, which means a huge reductionconsidering the high difficulty of the case studied: overlapped combination

of three compounds plus noise and the oxidation of containing media.

The presented work compared the performance of the proposed

alternative, with the more classical use of PCA preprocessing plus ANNs, or

the simpler case that downsamples the voltammogram as the way to reduce

the dimension of information before being coupled to ANNs. Calibration

models built with PCA-pretreated information did not perform well on

testing. To assure that an excess of 16 PCs was not the cause of bad generaliz-

ation capability of the ANN, we trained networks with only the first three PCsthat contained most of the input variance, and none of them reached the SSE

programmed for training. In the case of ANNs trained with wavelet coefficients

and downsampled voltammograms, the closeness of PRAEs obtained after

training is explained by the energy retained from original voltammograms

after being processed. When DWT or downsampling is applied to a signal,

the low frequency content is retained, and the original length of the signal is

reduced. In our case of study, the lengths of the processed voltammograms

were matched for comparison purposes, but the energy contents were

different. The bandwidth retained by DWT was longer than the one retainedby downsampling. This is explained if we consider that wavelet coefficients

are obtained by comparing a displacing base function against the signal

under observation. This means that this technique does not dismiss any segment

of the original voltammogram that might contain details closely related to the

amino acids. In contrast, downsampling removes points of the observed signal

after low-pass filtering that might contain amino-acid-related information.

Although the best calibration models are obtained when wavelet coeffi-

cients are used, downsampling can be taken as a very simple approach that

yields good models, probably due to the large ratio of measured data/chemical diversity obtained in voltammograms and indirect advantages

supplied by ANN modeling.

Of the three alternatives evaluated, the DWT–ANN combination was the

option that best performed for the voltammetric electronic tongue. The experi-

ence presented in this work is an interesting application of voltammetric

electronic tongues for the quantitative determination of chemical species.




REFERENCES

Apetrei, C., Rodriguez-Mendez, M.L., Parra, V., Gutierrez, F., and de Saja, J.A. 2004.Array of voltammetric sensors for the discrimination of bitter solutions. Sens.

Actuators, B103: 145–152.

Artursson, T. and Holmberg, M. 2002a. Wavelet transform of electronic tongue data.

Sens. Actuators, B87: 379–391.

Artursson, T., Spangeus, P., and Holmberg, M. 2002b. Variable reduction on electronic

tongue data. Anal. Chim. Acta, 452: 255–264.

Borggaard, C. and Thodberg, H.H. 1992. Optimal minimal neural interpretation of

spectra. Anal. Chem., 64: 545– 551.

Chau, F.-T., Liang, Y.-Z., Gao, J., and Shao, X.-G. 2004. Chemometrics. From Basics

to Wavelet Transform; Wiley-VCH: Weinheim.

Cocchi, M., Seeber, R., and Ulrici, A. 2003a. Multivariate calibration of signals by

WILMA. J. Chemom., 17: 512–527.

Cocchi, M., Hidalgo-Hidalgo-de-Cisneros, J.L., Naranjo-Rodrıguez, I., Palacios-

Santander, J.M., Seeber, R., and Ulrici, A. 2003b. Multicomponent analysis of

electrochemical signals in the wavelet domain. Talanta., 59: 735–749.

Collantes, E.R., Duta, R., Welsh, W.J., Zielinski, W.L., and Brower, J. 1997.

Preprocessing of HPLC trace impurity patterns by wavelet packets for pharma-

ceutical fingerprinting using artificial neural networks. Anal. Chem., 69: 1392– 1397.

Dathe, M. and Otto, M. 1996. Confidence intervals for calibration with neural

networks. Fresenius’ J. Anal. Chem., 356: 17–20.

de Carvalho, R.M., Mello, C., and Kubota, L.T. 2000. Simultaneous determination of phenol isomers in binary mixtures by differential pulse voltammetry using carbon

fibre electrode and neural network with pruning as a multivariate calibration tool.

Anal. Chim. Acta., 420: 109–121.

Demuth, H. and Beale, M. 2001. Neural Network Toolbox. User’s Guide; The

Mathworks Inc: Natick, MA.

Despagne, F. and Massart, D.L. 1998. Neural networks in multivariate calibration.

Analyst , 123: 157R–178R.

Distante, C., Leo, M., Siciliano, P., and Persaud, K.C. 2002. On the study of feature

extraction methods for an electronic nose. Sens. Actuators, B87: 274–288.

Ensafi, A.A., Khayamian, T., and Atabati, M. 2002. Simultaneous voltammetric deter-mination of molybdenum and copper by adsorption cathodic differential pulse

stripping method using a principal component artificial neural network. Talanta,

57: 785–793.

Gallardo, J., Alegret, S., de Roman, M.A., Munoz, R., Hernandez, P.R., Leija, L., and

del Valle, M. 2003. Determination of ammonium ion employing an electronic tongue

based on potentiometric sensors. Anal. Lett., 36 (14): 2893–2908.

Gemperline, P.J. 1997. Rugged spectroscopic calibration for process control.

Chemometr. Intell. Lab. Syst., 39: 29–40.

Gutes, A., Cespedes, F., Alegret, S., and del Valle, M. 2005. Determination of phenolic

compounds by a polyphenol oxidase amperometric biosensor and artificial neural

network analysis. Biosens. Bioelectron., 20: 1668–1673.

Holmberg, M., Eriksson, M., Krantz-Rulcker, C., Artursson, T., Winquist, F., Lloyd-

Spetz, A., and Lundstrom, I.. Second workshop of the second network on artificial

olfactory sensing (NOSE II). Sens. Actuators, B101: 213–223.

Holmin, S., Spangeus, P., Krantz-Rulcker, C., and Winquist, F. 2001. Compression of

electronic tongue data based on voltammetry—a comparative study. Sens. Actuators,

B76: 455–464.




Ionescu, R. and Llobet, E. 2002. Wavelet transform-based fast feature extraction from

temperature modulated semiconductor gas sensors. Sens. Actuators, B81: 289–295.

Krantz-Rulcker, C., Stenberg, M., Winquist, F., and Lundstrom, I. 2001. Electronic

tongues for environmental monitoring based on sensor arrays and pattern

recognition: a review. Anal. Chim. Acta., 426: 217–226.

Lavine, B.K. and Workman, J. 2002. Chemometrics. Anal. Chem., 74: 2763–2770.

Leung, A.K., Chau, F., and Gao, J. 1998. A review on applications of wavelet transform

techniques in chemical analysis: 1989 – 1997. Chemometr. Intell. Lab. Syst., 43:

165–184.

Mitra, S.K. 2001. Digital Signal Processing. A Computer-Based Approach, 2nd ed.;

McGraw-Hill: Boston, MA.

Moreno-Baron, L., Cartas, R., Merkoci, A., Alegret, S., del Valle, M., Leija, L.,

Hernandez, P.R., and Munoz, R. 2005. Application of the wavelet transform

coupled with artificial neural networks in a voltammetric electronic tongue. Sens. Actuators B (in press), (DOI:10.1016/ j.snb.2005.03.063).

Nie, L., Wu, S., and Wang, J. 2001. Continuous wavelet transform and its application to

resolving and quantifying the overlapped voltammetric peaks. Anal. Chim. Acta.,

450: 185–192.

Palacios-Santander, J.M., Jimenez-Jimenez, A., Cubillana-Aguilera, L.M., Naranjo-

Rodriguez, I., and Hidalgo-Hidalgo-de-Cisneros, J.L. 2003. Use of artificial neuralnetworks, aided by methods to reduce dimensions, to resolve overlapped electroche-

mical signals. A comparative study including other statistical methods. Mikrochim.

Acta, 142: 27–36.

Pravdova, V., Pravda, M., and Guilbault, G.G. 2002. Role of chemometrics for electro-chemical sensors. Anal. Lett., 35 (15): 2389–2419.

Shao, X. and Sun, L. 2001. An application of the continuous wavelet transform to

resolution of multicomponent overlapping analytical signals. Anal. Lett., 34:

267–280.

Shao, X., Leung, A.K., and Chau, F. 2003. Wavelet: a new trend in chemistry. Acc.

Chem. Res., 36: 276 – 283.

Shao, X., Pang, C., Wu, S., and Lin, X. 2000. Development of wavelet transform

voltammetric analyzer. Talanta., 50: 1175–1182.

Simons, J., Bos, M., and Van der Linden, W.E. 1995. Data processing for amperometric

signals. Analyst., 120: 1009–1012.

Winquist, F., Wide, P., and Lundstrom, I. 1997. An electronic tongue based on voltam-

metry. Anal. Chim. Acta., 357: 21–31.

Zhang, X. and Jin, J. 2004. Wavelet derivative: application in multicomponent analysis

of electrochemical signals. Electroanalysis., 16: 1514–1520.


Analytical Lett 2005

Documents

Transcript of Analytical Lett 2005